"Managing Wind-based Electricity Generation and Storage" – Helen Zhou, 2012
Among the many issues that profoundly affect the world economy every day, energy is one of the most prominent. Countries such as the U.S. strive to reduce reliance on the import of fossil fuels, and to meet increasing electricity demand without harming the environment.
Two of the most promising solutions for the energy issue are to rely on renewable energy, and to develop efficient electricity storage. Renewable energy—such as wind energy and solar energy—is free, abundant, and most importantly, does not exacerbate the global warming problem. However, most renewable energy is inherently intermittent and variable, and thus can benefit greatly from coupling with electricity storage, such as grid-level industrial batteries. Grid storage can also help match the supply and demand of an entire electricity market. In addition, electricity storage such as car batteries can help reduce dependence on oil, as it can enable the development of Plug-in Hybrid Electric Vehicles, and Battery Electric Vehicles. This thesis focuses on understanding how to manage renewable energy and electricity storage properly together, and electricity storage alone.
In Chapter 2, I study how to manage renewable energy, specifically wind energy. Managing wind energy is conceptually straightforward: generate and sell as much electricity as possible when prices are positive, and do nothing otherwise. However, this leads to curtailment when wind energy exceeds the transmission capacity, and possible revenue dilution when current prices are low but are expected to increase in the future. Electricity storage is being considered as a means to alleviate these problems, and also enables buying electricity from the market for later resale. But the presence of storage complicates the management of electricity generation from wind, and the value of storage for a wind-based generator is not entirely understood.
I demonstrate that for such a combined generation and storage system the optimal policy does not have any apparent structure, and that using overly simple policies can be considerably suboptimal. I thus develop and analyze a triple-threshold policy that I show to be nearoptimal. Using a financial engineering price model and calibrating it to data from the New York Independent System Operator, I show that storage can substantially increase the monetary value of a wind farm: If transmission capacity is tight, the majority of this value arises from reducing curtailment and time-shifting generation; if transmission capacity is abundant this value stems primarily from time-shifting generation and arbitrage. In addition, I find that while more storage capacity always increases the average energy sold to the market, it may actually decrease the average wind energy sold when transmission capacity is abundant.
In Chapter 3, I examine how electricity storage can be used to help match electricity supply and demand. Conventional wisdom suggests that when supply exceeds demand, any electricity surpluses should be stored for future resale. However, because electricity prices can be negative, another potential strategy of dealing with surpluses is to destroy them. Using real data, I find that for a merchant who trades electricity in a market, the strategy of destroying surpluses is potentially more valuable than the conventional strategy of storing surpluses.
In Chapter 4, I study how the operation and valuation of electricity storage facilities can be affected by their physical characteristics and operating dynamics. Examples are the degradation of energy capacity over time and the variation of round-trip efficiency at different charging/discharging rates. These dynamics are often ignored in the literature, thus it has not been established whether it is important to model these characteristics. Specifically, it remains an open question whether modeling these dynamics might materially change the prescribed operating policy and the resulting valuation of a storage facility. I answer this question using a representative setting, in which a battery is utilized to trade electricity in an energy arbitrage market.
Using engineering models, I capture energy capacity degradation and efficiency variation explicitly, evaluating three types of batteries: lead acid, lithium-ion, and Aqueous Hybrid Ion— a new commercial battery technology. I calibrate the model for each battery to manufacturers’ data and value these batteries using the same calibrated financial engineering price model as in Chapter 2. My analysis shows that: (a) it is quite suboptimal to operate each battery as if it did not degrade, particularly for lead acid and lithium-ion; (b) reducing degradation and efficiency variation have a complimentary effect: the value of reducing both together is greater than the sum of the value of reducing one individually; and (c) decreasing degradation may have a bigger effect than decreasing efficiency variation.
Contact: Helen Zhou Yangfang
Assistant Professor of Operations Management
Lee Kong Chian School of Business, Singapore Management University
"Managing Wind Power Forecast Uncertainty in Electric Grid" – Brandon Mauch, 2012
Electricity generated from wind power is both variable and uncertain. Wind forecasts provide valuable information for wind farm management, but they are not perfect. Chapter 2 presents a model of a wind farm with compressed air energy storage (CAES) participating freely in the day-ahead electricity market without the benefit of a renewable portfolio standard or production tax credit. CAES is used to reduce the risk of committing uncertain quantities of wind energy and to shift dispatch of wind generation to high price periods. Using wind forecast data and market prices from 2006 – 2009, we find that the annual income for the modeled wind-CAES system would not cover annualized capital costs. We also estimate market prices with a carbon price of $20 and $50 per tonne CO2 and find that the revenue would still not cover the capital costs. The implied cost per tonne of avoided CO2 to make a wind-CAES profitable from trading on the day-ahead market is roughly $100, with large variability due to electric power prices.
Wind power forecast errors for aggregated wind farms are often modeled with Gaussian distributions. However, data from several studies have shown this to be inaccurate. Further, the distribution of wind power forecast errors largely depends on the wind power forecast value. The few papers that account for this dependence bin the wind forecast data and fit parametric distributions to the actual wind power in each bin. A method to model wind power forecast uncertainty as a single closed-form solution using a logit transformation of historical wind power forecast and actual wind power data is presented in Chapter 3. Once transformed, the data become close to jointly normally distributed. We show the process of calculating confidence intervals for wind power forecast errors using the jointly normally distributed logit transformed data. This method has the advantage of fitting the entire dataset with five parameters while also providing the ability to make calculations conditioned on the value of the wind power forecast.
The model present in Chapter 3 is applied in Chapter 4 to calculate increases in net load uncertainty introduced from day-ahead wind power forecasts. Our analyses uses data from two different electric grids in the U.S. having similar levels of installed wind capacity with large differences in wind and load forecast accuracy due to geographic characteristics. A probabilistic method to calculate the dispatchable generation capacity required to balance day-ahead wind and load forecast errors for a given level of reliability is presented. Using empirical data we show that the capacity requirements for 95% day-ahead reliability range from 2100 MW to 5600 MW for ERCOT and 1900 MW to 4500 MW for MISO, depending on the amount of wind and load forecast for the next day. We briefly discuss the additional requirements for higher reliability levels and the effect of correlated wind and load forecast errors. Additionally, we show that each MW of additional wind power capacity in ERCOT must be matched by a 0.30 MW day-ahead dispatchable generation capacity to cover 95% of day-ahead uncertainty. Due to the lower wind forecast uncertainty in MISO, the value drops to 0.13 MW of dispatchable capacity for each MW of additional wind capacity.
Direct load control (DLC) has received a lot of attention lately as an enabler of wind power. One major benefit of DLC is the added flexibility it brings to the grid. Utilities in some parts of the U.S. can bid the load reduction from DLC into energy markets. Forecasts of the resource available for DLC assist in determining load reduction quantities to offer. In Chapter 5, we present a censored regression model to forecast load from residential air conditioners using historical load data, hour of the day, and ambient temperature. We tested the forecast model with hourly data from 467 air conditioners located in three different utilities. We used two months of data to train the model and then ran day-ahead forecasts over a six week period. Mean square errors ranged from 4% to 8% of mean air conditioner load. This method produced accurate forecasts with much lower data requirements than physics based forecast models.
Contact: Brandon Mauch
Utility Regulation Engineer
Iowa Utilities Board
1375 E. Court Ave.
Des Moines, IA 50319
"Topics in Residential Electric Demand Response" – Shira R. Horowitz, 2012
Demand response and dynamic pricing are touted as ways to empower consumers, save consumers money, and capitalize on the "smart grid" and expensive advanced meter infrastructure. In this work, I attempt to show that demand response and dynamic pricing are more nuanced. Dynamic pricing is very appealing in theory but the reality of it is less clear. Customers do not always respond to prices. Price differentials are not always large enough for customers to save money. Quantifying energy that was not used is difficult.
In chapter 2, I go into more detail on the potential benefits of demand response. I include a literature review of residential dynamic pilots and tariffs to see if there is evidence that consumers respond to dynamic rates, and assess the conditions that lead to a response.
Chapter 3 explores equity issues with dynamic pricing. Flat rates have an inherent cross-subsidy built in because more peaky customers (who use proportionally more power when marginal price is high) and less peaky customers pay the same rates, regardless of the cost they impose on the system. A switch to dynamic pricing would remove this cross subsidy and have a significant distributional impact. I analyze this distributional impact under different levels of elasticity and capacity savings.
Chapter 4 is an econometric analysis of the Commonwealth Edison RTP tariff. I show that it is extremely difficult to find the small signal of consumer response to price in all of the noise of everyday residential electricity usage.
Chapter 5 looks at methods for forecasting, measuring, and verifying demand response in direct load control of air-conditioners. Forecasting is important for system planning. Measurement and verification are necessary to ensure that payments are fair. I have developed a new, censored regression based model for forecasting the available direct load control resource. This forecast can be used for measurement and verification to determine AC load in the counterfactual where DLC is not applied. This method is more accurate than the typical moving averages used by most ISO's, and is simple, easy, and cheap to implement.
Contact: Shira Horowitz
PJM Interconnection, Inc.
"Integrating Variable Renewables into the Electric Grid: An Evaluation of Challenges and Potential Solutions" – Colleen A. Lueken, 2012
Renewable energy poses a challenge to electricity grid operators due to its variability and intermittency. In this thesis I quantify the cost of variability of different renewable energy technologies and then explore the use of reconfigurable distribution grids and pumped hydro electricity storage to integrate renewable energy into the electricity grid.
Cost of Variability
I calculate the cost of variability of solar thermal, solar photovoltaic, and wind by summing the costs of ancillary services and the energy required to compensate for variability and intermittency. I also calculate the cost of variability per unit of displaced CO2 emissions. The costs of variability are dependent on technology type. Variability cost for solar PV is $8-11/MWh, for solar thermal it is $5/MWh, and for wind it is around $4/MWh. Variability adds ~$15/tonne CO2 to the cost of abatement for solar thermal power, $25 for wind, and $33-$40 for PV.
Distribution Grid Reconfiguration
A reconfigurable network can change its topology by opening and closing switches on power lines. I show that reconfiguration allows a grid operator to reduce operational losses as well as accept more intermittent renewable generation than a static configuration can. Net present value analysis of automated switch technology shows that the return on investment is negative for this test network when considering loss reduction, but that the return is positive under certain conditions when reconfiguration is used to minimize curtailment of a renewable energy resource.
Pumped Hydro Storage in Portugal
Portugal is planning to build five new pumped hydro storage facilities to balance its growing wind capacity. I calculate the arbitrage potential of the storage capacity from the perspective of an independent storage owner, a thermal fleet owner, and a consumer-oriented storage owner. This research quantifies the effect storage ownership has on CO2 emissions, consumer electricity expenditure, and thermal generator profits. I find that in the Portuguese electricity market, an independent storage owner could not recoup its investment in storage using arbitrage only, but a thermal fleet owner or consumer-oriented owner may get a positive return on investment.
Contact: Colleen Lueken
"Energy Storage on the Grid and the Short-term Variability of Wind" – Eric Hittinger, 2012
Wind generation presents variability on every time scale, which must be accommodated by the electric grid. Limited quantities of wind power can be successfully integrated by the current generation and demand-side response mix but, as deployment of variable resources increases, the resulting variability becomes increasingly difficult and costly to mitigate. In Chapter 2, we model a co-located power generation/energy storage block composed of wind generation, a gas turbine, and fast-ramping energy storage. A scenario analysis identifies system configurations that can generate power with 30% of energy from wind, a variability of less than 0.5% of the desired power level, and an average cost around $70/MWh.
While energy storage technologies have existed for decades, fast-ramping grid-level storage is still an immature industry and is experiencing relatively rapid improvements in performance and cost across a variety of technologies. Decreased capital cost, increased power capability, and increased efficiency all would improve the value of an energy storage technology and each has cost implications that vary by application, but there has not yet been an investigation of the marginal rate of technical substitution between storage properties. The analysis in chapter 3 uses engineering-economic models of four emerging fast-ramping energy storage technologies to determine which storage properties have the greatest effect on cost-ofservice. We find that capital cost of storage is consistently important, and identify applications for which power/energy limitations are important.
In some systems with a large amount of wind power, the costs of wind integration have become significant and market rules have been slowly changing in order to internalize or control the variability of wind generation. Chapter 4 examines several potential market strategies for mitigating the effects of wind variability and estimate the effect that each strategy would have on the operation and profitability of wind farms. We find that market scenarios using existing v price signals to motivate wind to reduce variability allow wind generators to participate in variability reduction when the market conditions are favorable, and can reduce short-term (30- minute) fluctuations while having little effect on wind farm revenue.
Contact: Eric Hittinger
Assistant Professor of Public Policy
Rochester Institute of Technology
Office: Eastman 1-1313
92 Lomb Memorial Drive
Rochester, NY 14623-5604
"Does Tropical Cyclone Modification Make Sense? A Decision Analytic Perspective" – Kelly Klima, 2012
Recent dramatic increases in damages caused by tropical cyclones (TCs) and improved understanding of TC physics have led the Department of Homeland Security to fund research on intentional hurricane modification. Here I present a decision analytic assessment of whether hurricane modification is potentially cost effective in South Florida.
First, for a single storm I compare hardening buildings to lowering the wind speed of a TC by reducing sea surface temperatures with wind-wave pumps. I find that if it were feasible and properly implemented, modification could reduce net wind losses from an intense storm more than hardening structures. However, hardening provides "fail safe" protection for average storms that might not be achieved if the only option were modification. The effect of natural variability is larger than that of either strategy.
Second, for multiple storms over a given return period, I investigate TC wind and storm surge damage reduction by hardening buildings and by wind-wave pumps. The coastal areas examined experience more surge damages for short return periods, and more wind damages for long periods. Surge damages are best reduced through a surge barrier. Wind damages are best reduced by a portfolio of techniques including wind-wave pumps, assuming they work and are correctly deployed. Damages in areas outside of the floodplain will likely be dominated by wind damages, and hence a similar portfolio will likely be best in these areas.
Since hurricane modification might become a feasible strategy for reducing hurricane damages, to facilitate an informed and constructive discourse on implementation, policy makers need to understand how people perceive hurricane modification. Therefore using the mental models approach, I identified Florida residents’ perceptions of hurricane modification techniques. First, hurricane modification was perceived as a relatively ineffective strategy for vi damage reduction. Second, hurricane modification was expected to lead to changes in path, but not necessarily strength. Third, reported anger at hurricane modification was weaker when path was unaltered and the damages equal to or less than projected. Fourth, individuals who recognized the uncertainty inherent in hurricane prediction reported more anger at scientists across modification scenarios.
Contact: Kelly Klima
Climate Adaptation Policy Advisor
Center for Clean Air Policy
750 First Street NE
Washington, D.C. 20002
"Evaluating Interventions in the U.S. Electricity System: Assessments of Energy Efficiency, Renewable Energy, and Small-Scale Cogeneration" – Kyle Siler-Evans, 2012
There is growing interest in reducing the environmental and human-health impacts resulting from electricity generation. Renewable energy, energy efficiency, and energy conservation are all commonly suggested solutions. Such interventions may provide health and environmental benefits by displacing emissions from conventional power plants. However, the generation mix varies considerably from region to region and emissions vary by the type and age of a generator. Thus, the benefits of an intervention will depend on the specific generators that are displaced, which vary depending on the timing and location of the intervention.
Marginal emissions factors (MEFs) give a consistent measure of the avoided emissions per megawatt-hour of displaced electricity, which can be used to evaluate the change in emissions resulting from a variety of interventions. This thesis presents the first systematic calculation of MEFs for the U.S. electricity system. Using regressions of hourly generation and emissions data from 2006 through 2011, I estimate regional MEFs for CO2, NOx, and SO2, as well as the share of marginal generation from coal-, gas-, and oil-fired generators. This work highlights significant regional differences in the emissions benefits of displacing a unit of electricity: compared to the West, displacing one megawatt-hour of electricity in the Midwest is expected to avoid roughly 70% more CO2, 12 times more SO2, and 3 times more NOx emissions.
I go on to explore regional variations in the performance of wind turbines and solar panels, where performance is measured relative to three objectives: energy production, avoided CO2 emissions, and avoided health and environmental iii damages from criteria pollutants. For 22 regions of the United States, I use regressions of historic emissions and generation data to estimate marginal impact factors, a measure of the avoided health and environmental damages per megawatthour of displaced electricity. Marginal impact factors are used to evaluate the effects of an additional wind turbine or solar panel in the U.S. electricity system. I find that the most attractive sites for renewables depend strongly on one’s objective. A solar panel in Iowa displaces 20% more CO2 emissions than a panel in Arizona, though energy production from the Iowa panel is 25% less. Similarly, despite a modest wind resource, a wind turbine in West Virginia is expected to displace 7 times more health and environmental damages than a wind turbine in Oklahoma.
Finally, I shift focus and explore the economics of small-scale cogeneration, which has long been recognized as a more efficient alternative to central-station power. Although the benefits of distributed cogeneration are widely cited, adoption has been slow in the U.S. Adoption could be encouraged by making cogeneration more economically attractive, either by increasing the expected returns or decreasing the risks of such investments. I present a case study of a 300-kilowatt cogeneration unit and evaluate the expected returns from: demand response, capacity markets, regulation markets, accelerated depreciation, a price on CO2 emissions, and net metering. In addition, I explore the effectiveness of feed-in tariffs at mitigating the energy-price risks to cogeneration projects.
Contact: Kyle Siler-Evans
Carnegie Mellon University
"Plug-In Hybrid Electric Vehicles: Battery Degradation, Grid Support, Emissions, and Battery Size Tradeoffs" – Scott B. Peterson, 2012
Plug-in hybrid electric vehicles (PHEVs) may become a substantial part of the transportation fleet on time scales of a decade or two. This dissertation investigates battery degradation, and how the introduction of PHEVs may influence the electricity grid, emissions, and petroleum use in the US. It examines the effects of combined driving and vehicle-to-grid (V2G) usage on the lifetime performance of relevant commercial Li-ion cells. The loss of battery capacity was quantified as a function of driving days as well as a function of integrated capacity and energy processed by the cells. The cells tested showed promising capacity fade performance: more than 95% of the original cell capacity remains after thousands of driving days worth of use. Statistical analyses indicate that rapid vehicle motive cycling degraded the cells more than slower, V2G galvanostatic cycling. These data are used to examine the potential economic implications of using vehicle batteries to store grid electricity generated at off-peak hours for off-vehicle use during peak hours. The maximum annual profit with perfect market information and no battery degradation cost ranged from ~US$140 to $250 in the three cities. If the measured battery degradation is applied, however, the maximum annual profit decreases to ~$10–120. The dissertation details the increase in electric grid load and emissions due to vehicle battery charging in PJM and NYISO with the current generation mix, the current mix with a $50/tonne CO2 price, and this case but with existing coal generators retrofitted with 80% CO2 capture. It also models emissions using natural gas or wind+gas. PHEV fleet percentages between 0.4 and 50% are examined. When compared to 2020 CAFE standards, net CO2 emissions in New York are reduced by switching from gasoline to electricity; coal-heavy PJM shows somewhat smaller benefits unless coal units are fitted with CCS or replaced with lower CO2 generation. NOX is reduced in both RTOs, but there is upward pressure on SO2 emissions or allowance prices under a cap. Finally the dissertation compares increasing the all-electric range (AER) of PHEVs to installing charging infrastructure. Fuel use was modeled using the National Household Travel Survey and Greenhouse Gasses, Regulated Emissions, and Energy Use in Transportation model. It was found that increasing AER of plug-in hybrids was a more cost effective solution to reducing gasoline consumption than installing charging infrastructure. Comparison of results to current subsidy structure shows various options to improve future PHEV or other vehicle subsidy programs.
Contact: Scott Peterson
Carnegie Mellon University
"Energy Efficiency and Rebound Effects in the United States: Implications for Renewables Investment and Emissions Abatement" – Brinda Ann Thomas, 2012
By lowering the energy required to provide a service, energy efficiency can help society consume less energy, emit less CO2e and other air pollutants, while maintaining quality of life. In this work, I examine a key benefit of energy efficiency, reducing renewables investment costs, and a side-effect, expanding energy service demand, also known as the rebound effect.
First, I assess the economics of an energy efficiency intervention, using dedicated direct current (DC) circuits to operate lighting in commercial buildings. I find that using DC circuits in grid-connected PV-powered LED lighting systems can lower the total unsubsidized capital costs by 4% to 21% and levelized annual costs by 2% to 21% compared to AC grid-connected PV LEDs providing the same level of lighting service. I also explore the barriers and limitations of DC circuits in commercial buildings.
Second, I examine the rebound effect from residential energy efficiency investments through a model in which households re-spend energy expenditure savings from an efficiency investment on more of the energy service (direct rebound) or on other goods and services (indirect rebound).
Using U.S. household expenditure data and environmentally-extended input-output analysis, I find indirect rebound effects in CO2e emissions of 5-15%, depending on the fuel saved and assuming a 10% direct rebound.
Third, I examine the variation in the indirect rebound from electricity efficiency across U.S. states due to differences in electric grid mix, fuel prices, household income, and spending patterns. I find that the CO2e direct and indirect rebound effects vary across states between 6-40%, when including full supply chain emissions, and between 4-30% when including only combustion and electricity emissions.
I conclude that energy efficiency can provide significant benefits for reducing energy expenditures, CO2e and other pollutants, and renewables investment costs under policy mandates, even after accounting for the rebound effect. While the CO2e rebound effect is currently modest in the U.S., there are some exceptions that may be relevant for energy efficiency policy assessments. In addition, more data collection and measurements of direct rebound effects are needed, especially in developing countries where the demand for energy services has not fully been met.